The present application generally relates to manufacturing semiconductor products. More particularly, the present application relates to determining a group of semiconductor manufacturing process steps with a similar influence on individual semiconductor products.
A semiconductor product includes, but is not limited to a semiconductor chip, a semiconductor wafer, and a semiconductor wafer lot. A similar influence of queue time (“Q-time”) on individual semiconductor products includes, but is not limited to increasing or decreasing leakage current, increasing or decreasing threshold voltage, increasing or decreasing areas of the semiconductor products, increasing or decreasing operational frequencies of the semiconductor products, etc. Q-time refers to the time spent for a semiconductor product to wait between individual semiconductor manufacturing processes. Maintaining high manufacturing yields and precise product quality control are of utmost importance in a commercial manufacture (e.g., IBM®, etc.) of semiconductor products. Unfortunately, an extraordinary sophistication of individual fabrication processes and an extreme complexity of an overall integrated fabrication process present myriad mechanisms for defects to be introduced which may degrade semiconductor product performance or render a semiconductor product non-functional. Thus, manufacturing yields are frequently lower than desired and product performance distributions are wider than desired.
A vast scale and scope of defect generating mechanisms also lead to extreme difficulties in diagnosing a source of defects, and many defects in semiconductor product manufacturing processes go undiagnosed. For example, a traditional diagnostic procedure may attempt a full evaluation of entire parameters (e.g., leakage current, capacitance, frequency, area, etc.) in an entire semiconductor manufacturing process. However, any such attempt may involve an impossible amount of data and combinatorially growing dependence structures (e.g., data dependency). Thus, to reduce the amount of computation, typical diagnostic or monitoring methods focus on specific subsets of production data such as production logistics and/or process trace data. An example of production logistics data is that a semiconductor wafer “X” in a semiconductor lot “Y” at time “T1” entered a chamber “C” in a semiconductor manufacturing tool “A” where a semiconductor manufacturing process “P” following a recipe “R” was performed as a semiconductor manufacturing step “S” until time “T2”. Examples of the trace data include, but are not limited to chamber pressure, chamber temperature, and chamber atmosphere composition.
Q-time can vary widely in a semiconductor manufacturing environment, reflecting different line loading and tool availabilities. Significant product defects can be associated with the queue times between particular process steps in the semiconductor manufacturing process. For example, epitaxial thin film growth can be influenced by a pre-clean to deposition queue time, as a result of an uncontrolled growth of native oxides, especially on sidewalls. Back end metallization can suffer serious corrosion if a post-polish queue time is not maintained below a critical threshold. Migration of RIE (Reactive Ion Etching) induced contamination from photoresist is observed if an etch to strip queue time is not controlled.
Traditionally, the discovery of these effects (i.e., Q-time effects on semiconductor manufacturing process) has been a result of a painstaking ad-hoc investigation of otherwise unexplainable aberrant manufacturing results. Typically, it is difficult to anticipate an impact of queue times on some semiconductor manufacturing processes now in development. Traditionally, when discovered and adequately explored the Q-time effects, manufacturing line controls are introduced to assure semiconductor manufacturing processes take place within acceptable queue time windows.
Typical semiconductor manufacturing lines may include several thousands of steps, representing as many as 1,000,000 queue times. While the queue times between some particular steps are known to be critical, in general, product quality is not expected to be a sensitive function of queue time. These two factors (i.e., queue times and the product quality) contribute to a status quo in semiconductor product manufacturing. By analyzing or monitoring the semiconductor manufacturing process, the impact of queue time on product quality and/or manufacturing yields can be obtained, e.g., identifying pairs or groups of semiconductor manufacturing process steps with dependent (i.e., correlated) Q-times, and hence pointing to a group of steps with a possible influence on the product quality.